title: Exploring optimal current stimuli that provide membrane voltage tracking in a neuron model.
abstract: Studying neurons from an energy efficiency perspective has produced results in the research literature. This paper presents a method that enables computation of low energy input current stimuli that are able to drive a reduced Hodgkin-Huxley neuron model to approximate a prescribed time-varying reference membrane voltage. An optimal control technique is used to discover an input current that optimally minimizes a user selected balance between the square of the input stimulus current (input current 'energy') and the difference between the reference voltage and the membrane voltage (tracking error) over a stimulation period. Selecting reference signals to be membrane voltages produced by the neuron model in response to common types of input currents i(t) enables a comparison between i(t) and the determined optimal current stimulus i*(t). The intent is not to modify neuron dynamics, but through comparison of i(t) and i*(t) provide insight into neuron dynamics. Simulation results for four different bifurcation types demonstrate that this method consistently finds lower energy stimulus currents i*(t) that are able to approximate membrane voltages as produced by higher energy input currents i(t) in this neuron model.
authors.affiliation: Department of Electrical and Computer Engineering, Western Michigan University, Kalamazoo, MI 49008, USA.
authors.forename: M
authors.initials: M
authors.lastname: Ellinger
authors.forename: M E
authors.initials: ME
authors.lastname: Koelling
authors.forename: D A
authors.initials: DA
authors.lastname: Miller
authors.forename: F L
authors.initials: FL
authors.lastname: Severance
authors.forename: J
authors.initials: J
authors.lastname: Stahl
mesh_terms: D000200:Action Potentials / Q000502:physiology*; D004558:Electric Stimulation / Q000379:methods*; D008433:Mathematics; D008564:Membrane Potentials / Q000502:physiology*; D008959:Models, Neurological; D009474:Neurons / Q000502:physiology*